Hierarchical sampling for object identification

Abstract

Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.

What is claimed is: 
     
         1 . A method of identifying targets, comprising:
 receiving a first plurality of snapshots;   generating a first plurality of descriptors each associated with the first plurality of snapshots;   grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors;   selecting a representative snapshot for each of the at least one cluster;   generating at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and   identifying a target based on comparing the at least second descriptor and a third descriptor.   
     
     
         2 . The method of  claim 1 , wherein the third descriptor is associated with a second plurality of snapshots or an input query. 
     
     
         3 . The method of  claim 2 , wherein the input query includes one or more arrays of numbers representing an intended target. 
     
     
         4 . The method of  claim 1 , further comprising, prior to receiving the first plurality of snapshots:
 receiving a second plurality of snapshots;   generating a third plurality of descriptors each associated with the second plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;   grouping the second plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and   selecting a second plurality of representative snapshots as the first plurality of snapshots.   
     
     
         5 . The method of  claim 1 , further comprising:
 classifying the representative snapshot for each of the at least one cluster;   aggregating classification scores of the representative snapshot for each of the at least one cluster;   determining a class based on the aggregated classification scores; and   wherein the at least one descriptor is a class-specific descriptor.   
     
     
         6 . The method of  claim 1 , further comprising, prior to selecting the representative snapshot, estimating a mean average precision (MAP) for each snapshot in the at least one cluster. 
     
     
         7 . The method of  claim 6 , wherein selecting the representative snapshot for each of the at least one cluster comprises selecting a snapshot in the at least one cluster having a highest estimated MAP. 
     
     
         8 . The method of  claim 6 , wherein estimating a MAP comprises using a neural network to estimate the MAP. 
     
     
         9 . A non-transitory computer readable medium comprising instructions stored therein that, when executed by a processor of a system, cause the processor to:
 receive a first plurality of snapshots;   generate a first plurality of descriptors each associated with the first plurality of snapshots;   group the first plurality of snapshots into at least one cluster based on the plurality of descriptors;   select a representative snapshot for each of the at least one cluster;   generate at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and   identify a target based on comparing the at least second descriptor and a third descriptor.   
     
     
         10 . The non-transitory computer readable medium of  claim 9 , wherein the third descriptor is associated with a second plurality of snapshots or an input query. 
     
     
         11 . The non-transitory computer readable medium of  claim 10 , wherein the input query includes one or more arrays of numbers representing an intended target. 
     
     
         12 . The non-transitory computer readable medium of  claim 9 , further comprising instructions that, prior to receiving the first plurality of snapshots, cause the processor to:
 receive a second plurality of snapshots;   generate a third plurality of descriptors each associated with the second plurality of snapshots, wherein the third plurality of descriptors are less complex than the first plurality of descriptors;   group the second plurality of snapshots into a plurality of clusters based on the third plurality of descriptors; and   select a second plurality of representative snapshots as the first plurality of snapshots.   
     
     
         13 . The non-transitory computer readable medium of  claim 9 , further comprising instructions that cause the processor to:
 classify the representative snapshot for each of the at least one cluster;   aggregate classification scores of the representative snapshot for each of the at least one cluster;   determine a class based on the aggregated classification scores; and   wherein the at least one descriptor is a class-specific descriptor.   
     
     
         14 . The non-transitory computer readable medium of  claim 9 , further comprising instructions that, prior to selecting the representative snapshot, cause to processor to estimate a mean average precision (MAP) for each snapshot in the at least one cluster. 
     
     
         15 . The non-transitory computer readable medium of  claim 14 , wherein the instructions for selecting the representative snapshot for each of the at least one cluster comprises instructions for selecting a snapshot in the at least one cluster having a highest estimated MAP. 
     
     
         16 . The non-transitory computer readable medium of  claim 14 , wherein the instructions for estimating a MAP comprises instructions for using a neural network to estimate the MAP. 
     
     
         17 . A system, comprising:
 memory that stores instructions; and   a processor configured to execute the instructions to:
 receive a first plurality of snapshots; 
 generate a first plurality of descriptors each associated with the first plurality of snapshots; 
 group the first plurality of snapshots into at least one cluster based on the plurality of descriptors; 
 select a representative snapshot for each of the at least one cluster; 
 generate at least one second descriptor for each representative snapshot, wherein the at least one second descriptor is more complex than the first plurality of descriptors; and 
 identify a target based on comparing the at least second descriptor and a third descriptor. 
   
     
     
         18 . The system of  claim 17 , wherein the third descriptor is associated with a second plurality of snapshots or an input query. 
     
     
         19 . The system of  claim 18 , wherein the input query includes one or more arrays of numbers representing an intended target. 
     
     
         20 . The system of  claim 17 , wherein the processor is further configured to, prior to selecting the representative snapshot, estimate a mean average precision (MAP) for each snapshot in the at least one cluster using a neural network.